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  <div class="section" id="mindspore-ops-adamweightdecay">
<h1>mindspore.ops.AdamWeightDecay<a class="headerlink" href="#mindspore-ops-adamweightdecay" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="mindspore.ops.AdamWeightDecay">
<em class="property">class </em><code class="sig-prename descclassname">mindspore.ops.</code><code class="sig-name descname">AdamWeightDecay</code><span class="sig-paren">(</span><em class="sig-param">use_locking=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mindspore/ops/operations/nn_ops.html#AdamWeightDecay"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mindspore.ops.AdamWeightDecay" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates gradients by the Adaptive Moment Estimation algorithm with weight decay (AdamWeightDecay).</p>
<p>The Adam algorithm is proposed in <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a>.
The AdamWeightDecay variant was proposed in <a class="reference external" href="https://arxiv.org/abs/1711.05101">Decoupled Weight Decay Regularization</a>.</p>
<p>The updating formulas are as follows,</p>
<div class="math notranslate nohighlight">
\[\begin{split}\begin{array}{ll} \\
    m = \beta_1 * m + (1 - \beta_1) * g \\
    v = \beta_2 * v + (1 - \beta_2) * g * g \\
    update = \frac{m}{\sqrt{v} + \epsilon} \\
    update =
    \begin{cases}
        update + weight\_decay * w
            &amp; \text{ if } weight\_decay &gt; 0 \\
        update
            &amp; \text{ otherwise }
    \end{cases} \\
    w  = w - lr * update
\end{array}\end{split}\]</div>
<p><span class="math notranslate nohighlight">\(m\)</span> represents the 1st moment vector, <span class="math notranslate nohighlight">\(v\)</span> represents the 2nd moment vector, <span class="math notranslate nohighlight">\(g\)</span> represents
<cite>gradient</cite>, <span class="math notranslate nohighlight">\(\beta_1, \beta_2\)</span> represent <cite>beta1</cite> and <cite>beta2</cite>,
<span class="math notranslate nohighlight">\(lr\)</span> represents <cite>learning_rate</cite>, <span class="math notranslate nohighlight">\(w\)</span> represents <cite>var</cite>, <span class="math notranslate nohighlight">\(decay\)</span> represents <cite>weight_decay</cite>,
<span class="math notranslate nohighlight">\(\epsilon\)</span> represents <cite>epsilon</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>use_locking</strong> (<a class="reference external" href="https://docs.python.org/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a>) – Whether to enable a lock to protect variable tensors from being updated.
If true, updates of the var, m, and v tensors will be protected by a lock.
If false, the result is unpredictable. Default: False.</p>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>var</strong> (Tensor) - Weights to be updated. The shape is <span class="math notranslate nohighlight">\((N, *)\)</span> where <span class="math notranslate nohighlight">\(*\)</span> means,
any number of additional dimensions. The data type can be float16 or float32.</p></li>
<li><p><strong>m</strong> (Tensor) - The 1st moment vector in the updating formula,
the shape and data type value should be the same as <cite>var</cite>.</p></li>
<li><p><strong>v</strong> (Tensor) - the 2nd moment vector in the updating formula,
the shape and data type value should be the same as <cite>var</cite>. Mean square gradients with the same type as <cite>var</cite>.</p></li>
<li><p><strong>lr</strong> (float) - <span class="math notranslate nohighlight">\(l\)</span> in the updating formula. The paper suggested value is <span class="math notranslate nohighlight">\(10^{-8}\)</span>,
the data type value should be the same as <cite>var</cite>.</p></li>
<li><p><strong>beta1</strong> (float) - The exponential decay rate for the 1st moment estimations,
the data type value should be the same as <cite>var</cite>. The paper suggested value is <span class="math notranslate nohighlight">\(0.9\)</span></p></li>
<li><p><strong>beta2</strong> (float) - The exponential decay rate for the 2nd moment estimations,
the data type value should be the same as <cite>var</cite>. The paper suggested value is <span class="math notranslate nohighlight">\(0.999\)</span></p></li>
<li><p><strong>epsilon</strong> (float) - Term added to the denominator to improve numerical stability.</p></li>
<li><p><strong>decay</strong> (float) - The weight decay value, must be a scalar tensor with float data type.
Default: 0.0.</p></li>
<li><p><strong>gradient</strong> (Tensor) - Gradient, has the same shape and data type as <cite>var</cite>.</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><p>Tuple of 3 Tensor, the updated parameters.</p>
<ul class="simple">
<li><p><strong>var</strong> (Tensor) - The same shape and data type as <cite>var</cite>.</p></li>
<li><p><strong>m</strong> (Tensor) - The same shape and data type as <cite>m</cite>.</p></li>
<li><p><strong>v</strong> (Tensor) - The same shape and data type as <cite>v</cite>.</p></li>
</ul>
</dd>
<dt>Supported Platforms:</dt><dd><p><code class="docutils literal notranslate"><span class="pre">GPU</span></code> <code class="docutils literal notranslate"><span class="pre">CPU</span></code></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mindspore.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">Parameter</span><span class="p">,</span> <span class="n">ops</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
<span class="gp">... </span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="gp">... </span>        <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">adam_weight_decay</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">AdamWeightDecay</span><span class="p">()</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">var</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;var&quot;</span><span class="p">)</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;m&quot;</span><span class="p">)</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">v</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;v&quot;</span><span class="p">)</span>
<span class="gp">... </span>    <span class="k">def</span> <span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">grad</span><span class="p">):</span>
<span class="gp">... </span>        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">adam_weight_decay</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">var</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">v</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="p">,</span>
<span class="gp">... </span>                              <span class="n">epsilon</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">grad</span><span class="p">)</span>
<span class="gp">... </span>        <span class="k">return</span> <span class="n">out</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">gradient</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">,</span> <span class="mf">1e-8</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">gradient</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">var</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="go">[[0.999 0.999]</span>
<span class="go"> [0.999 0.999]]</span>
</pre></div>
</div>
</dd></dl>

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